Overview

Dataset statistics

Number of variables17
Number of observations71102
Missing cells26294
Missing cells (%)2.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.7 MiB
Average record size in memory143.6 B

Variable types

NUM10
CAT7

Warnings

anio has constant value "71102" Constant
colonia has a high cardinality: 1340 distinct values High cardinality
consumo_prom_no_dom is highly correlated with consumo_promHigh correlation
consumo_prom is highly correlated with consumo_prom_no_domHigh correlation
alcaldia is highly correlated with nomgeoHigh correlation
nomgeo is highly correlated with alcaldiaHigh correlation
consumo_total_mixto has 8327 (11.7%) missing values Missing
consumo_prom_dom has 4820 (6.8%) missing values Missing
consumo_total_dom has 4820 (6.8%) missing values Missing
consumo_prom_mixto has 8327 (11.7%) missing values Missing
consumo_total_mixto is highly skewed (γ1 = 21.76535468) Skewed
consumo_prom_dom is highly skewed (γ1 = 74.81862948) Skewed
consumo_prom_mixto is highly skewed (γ1 = 43.60044406) Skewed
consumo_prom is highly skewed (γ1 = 43.38268186) Skewed
consumo_prom_no_dom is highly skewed (γ1 = 40.71654298) Skewed
consumo_total_no_dom is highly skewed (γ1 = 22.5073679) Skewed
gid has unique values Unique
consumo_total_mixto has 17715 (24.9%) zeros Zeros
consumo_prom_dom has 9861 (13.9%) zeros Zeros
consumo_total_dom has 9861 (13.9%) zeros Zeros
consumo_prom_mixto has 17715 (24.9%) zeros Zeros
consumo_total has 2451 (3.4%) zeros Zeros
consumo_prom has 2451 (3.4%) zeros Zeros
consumo_prom_no_dom has 8109 (11.4%) zeros Zeros
consumo_total_no_dom has 8109 (11.4%) zeros Zeros

Reproduction

Analysis started2020-09-30 21:17:30.399226
Analysis finished2020-09-30 21:17:46.826647
Duration16.43 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

consumo_total_mixto
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct24339
Distinct (%)38.8%
Missing8327
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean174.3599291
Minimum0
Maximum23404.44
Zeros17715
Zeros (%)24.9%
Memory size555.5 KiB
2020-09-30T16:17:46.931469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median79.94
Q3233.32
95-th percentile660.779
Maximum23404.44
Range23404.44
Interquartile range (IQR)233.32

Descriptive statistics

Standard deviation312.663596
Coefficient of variation (CV)1.793207864
Kurtosis1419.360189
Mean174.3599291
Median Absolute Deviation (MAD)79.94
Skewness21.76535468
Sum10945444.55
Variance97758.52424
MonotocityNot monotonic
2020-09-30T16:17:47.094159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01771524.9%
 
36740.1%
 
17.7610.1%
 
36.6590.1%
 
18.3540.1%
 
29.28520.1%
 
57.96500.1%
 
23.8480.1%
 
43.32470.1%
 
46.98460.1%
 
Other values (24329)4456962.7%
 
(Missing)832711.7%
 
ValueCountFrequency (%) 
01771524.9%
 
0.121< 0.1%
 
0.244< 0.1%
 
0.273< 0.1%
 
0.354< 0.1%
 
ValueCountFrequency (%) 
23404.441< 0.1%
 
23058.91< 0.1%
 
23031.061< 0.1%
 
5979.711< 0.1%
 
5974.321< 0.1%
 

anio
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size555.5 KiB
2019
71102 
ValueCountFrequency (%) 
201971102100.0%
 
2020-09-30T16:17:47.237224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T16:17:47.319790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:47.403980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

nomgeo
Categorical

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size70.2 KiB
iztapalapa
10515 
gustavo a. madero
10058 
cuauhtemoc
7313 
benito juarez
6049 
venustiano carranza
5179 
Other values (11)
31988 
ValueCountFrequency (%) 
iztapalapa1051514.8%
 
gustavo a. madero1005814.1%
 
cuauhtemoc731310.3%
 
benito juarez60498.5%
 
venustiano carranza51797.3%
 
miguel hidalgo51107.2%
 
coyoacan49477.0%
 
azcapotzalco42165.9%
 
alvaro obregon41405.8%
 
iztacalco34694.9%
 
Other values (6)1010614.2%
 
2020-09-30T16:17:47.543239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T16:17:47.697460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length12
Mean length12.43351804
Min length7

consumo_prom_dom
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct52060
Distinct (%)78.5%
Missing4820
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean29.13238577
Minimum0
Maximum7796.41
Zeros9861
Zeros (%)13.9%
Memory size555.5 KiB
2020-09-30T16:17:47.884216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118.69054691
median26.41424809
Q336.24656251
95-th percentile59.39294171
Maximum7796.41
Range7796.41
Interquartile range (IQR)17.5560156

Descriptive statistics

Standard deviation64.56592495
Coefficient of variation (CV)2.216293765
Kurtosis7663.654738
Mean29.13238577
Median Absolute Deviation (MAD)8.738705357
Skewness74.81862948
Sum1930952.794
Variance4168.758665
MonotocityNot monotonic
2020-09-30T16:17:48.057833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0986113.9%
 
1.2233< 0.1%
 
14.6423< 0.1%
 
10.9822< 0.1%
 
15.2522< 0.1%
 
9.7621< 0.1%
 
9.1521< 0.1%
 
20.4820< 0.1%
 
7.9320< 0.1%
 
11.5920< 0.1%
 
Other values (52050)5621979.1%
 
(Missing)48206.8%
 
ValueCountFrequency (%) 
0986113.9%
 
0.0099999997761< 0.1%
 
0.021< 0.1%
 
0.122< 0.1%
 
0.12999999521< 0.1%
 
ValueCountFrequency (%) 
7796.411< 0.1%
 
7581.691< 0.1%
 
6073.4599611< 0.1%
 
3726.51< 0.1%
 
3622.21< 0.1%
 

consumo_total_dom
Real number (ℝ≥0)

MISSING
ZEROS

Distinct47051
Distinct (%)71.0%
Missing4820
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean1186.263611
Minimum0
Maximum95060.69
Zeros9861
Zeros (%)13.9%
Memory size555.5 KiB
2020-09-30T16:17:48.256061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1161.635
median604.185
Q31261.445
95-th percentile4027.52
Maximum95060.69
Range95060.69
Interquartile range (IQR)1099.81

Descriptive statistics

Standard deviation2771.038307
Coefficient of variation (CV)2.33593805
Kurtosis248.0413047
Mean1186.263611
Median Absolute Deviation (MAD)517.3
Skewness12.52320362
Sum78627924.68
Variance7678653.301
MonotocityNot monotonic
2020-09-30T16:17:48.419740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0986113.9%
 
1.22370.1%
 
10.9821< 0.1%
 
3.6620< 0.1%
 
14.6420< 0.1%
 
25.6220< 0.1%
 
18.319< 0.1%
 
15.2519< 0.1%
 
7.9319< 0.1%
 
17.6918< 0.1%
 
Other values (47041)5622879.1%
 
(Missing)48206.8%
 
ValueCountFrequency (%) 
0986113.9%
 
0.121< 0.1%
 
0.241< 0.1%
 
0.52< 0.1%
 
0.61< 0.1%
 
ValueCountFrequency (%) 
95060.691< 0.1%
 
94021.71< 0.1%
 
90078.441< 0.1%
 
83309.941< 0.1%
 
82689.381< 0.1%
 

alcaldia
Categorical

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size70.2 KiB
iztapalapa
10515 
gustavo a. madero
10058 
cuauhtemoc
7313 
benito juarez
6049 
venustiano carranza
5179 
Other values (11)
31988 
ValueCountFrequency (%) 
iztapalapa1051514.8%
 
gustavo a. madero1005814.1%
 
cuauhtemoc731310.3%
 
benito juarez60498.5%
 
venustiano carranza51797.3%
 
miguel hidalgo51107.2%
 
coyoacan49477.0%
 
azcapotzalco42165.9%
 
alvaro obregon41405.8%
 
iztacalco34694.9%
 
Other values (6)1010614.2%
 
2020-09-30T16:17:48.579666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T16:17:48.890545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length12
Mean length12.25522489
Min length7

colonia
Categorical

HIGH CARDINALITY

Distinct1340
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size189.3 KiB
centro
 
1139
agricola oriental
 
837
roma norte
 
602
moctezuma 2a seccion
 
558
jardin balbuena
 
498
Other values (1335)
67468 
ValueCountFrequency (%) 
centro11391.6%
 
agricola oriental8371.2%
 
roma norte6020.8%
 
moctezuma 2a seccion5580.8%
 
jardin balbuena4980.7%
 
doctores4900.7%
 
san felipe de jesus4190.6%
 
roma sur4180.6%
 
obrera4180.6%
 
agricola pantitlan4170.6%
 
Other values (1330)6530691.8%
 
2020-09-30T16:17:49.038338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2020-09-30T16:17:49.214963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length43
Median length16
Mean length16.86555934
Min length4

consumo_prom_mixto
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct31911
Distinct (%)50.8%
Missing8327
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean50.63623377
Minimum0
Maximum11702.22
Zeros17715
Zeros (%)24.9%
Memory size555.5 KiB
2020-09-30T16:17:49.390232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median33.45166667
Q361.21654793
95-th percentile162.2529989
Maximum11702.22
Range11702.22
Interquartile range (IQR)61.21654793

Descriptive statistics

Standard deviation130.4086734
Coefficient of variation (CV)2.575402309
Kurtosis3263.991441
Mean50.63623377
Median Absolute Deviation (MAD)33.33333333
Skewness43.60044406
Sum3178689.575
Variance17006.42209
MonotocityNot monotonic
2020-09-30T16:17:49.558760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01771524.9%
 
36580.1%
 
29.28570.1%
 
36.6530.1%
 
23.8490.1%
 
25.62480.1%
 
26.84470.1%
 
18.92450.1%
 
1.84450.1%
 
11.6450.1%
 
Other values (31901)4461362.7%
 
(Missing)832711.7%
 
ValueCountFrequency (%) 
01771524.9%
 
0.11999999732< 0.1%
 
0.18999999761< 0.1%
 
0.192< 0.1%
 
0.23999999461< 0.1%
 
ValueCountFrequency (%) 
11702.221< 0.1%
 
11529.449711< 0.1%
 
11515.531< 0.1%
 
58083< 0.1%
 
4919.041< 0.1%
 

consumo_total
Real number (ℝ≥0)

ZEROS

Distinct56015
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1695.847222
Minimum0
Maximum119726.94
Zeros2451
Zeros (%)3.4%
Memory size555.5 KiB
2020-09-30T16:17:49.748405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.49
Q1340.9525
median896.175
Q31808.9025
95-th percentile5564.1965
Maximum119726.94
Range119726.94
Interquartile range (IQR)1467.95

Descriptive statistics

Standard deviation3555.697457
Coefficient of variation (CV)2.096708601
Kurtosis195.8775277
Mean1695.847222
Median Absolute Deviation (MAD)664.505
Skewness10.99825971
Sum120578129.2
Variance12642984.41
MonotocityNot monotonic
2020-09-30T16:17:49.923534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
024513.4%
 
3.05700.1%
 
1.22680.1%
 
3.66420.1%
 
6.71410.1%
 
1.83400.1%
 
7.93390.1%
 
6.1360.1%
 
9.76360.1%
 
4.88360.1%
 
Other values (56005)6824396.0%
 
ValueCountFrequency (%) 
024513.4%
 
0.013< 0.1%
 
0.053< 0.1%
 
0.125< 0.1%
 
0.2418< 0.1%
 
ValueCountFrequency (%) 
119726.941< 0.1%
 
117150.911< 0.1%
 
1010351< 0.1%
 
95117.771< 0.1%
 
94078.21< 0.1%
 

consumo_prom
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct62214
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.2173991
Minimum0
Maximum89691.77344
Zeros2451
Zeros (%)3.4%
Memory size555.5 KiB
2020-09-30T16:17:50.120401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.867208333
Q123.01013907
median31.69381809
Q345.48491686
95-th percentile188.219501
Maximum89691.77344
Range89691.77344
Interquartile range (IQR)22.47477779

Descriptive statistics

Standard deviation1069.949262
Coefficient of variation (CV)9.620340614
Kurtosis2599.541185
Mean111.2173991
Median Absolute Deviation (MAD)10.31349875
Skewness43.38268186
Sum7907779.51
Variance1144791.422
MonotocityNot monotonic
2020-09-30T16:17:50.282441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
024513.4%
 
1.22620.1%
 
3.05550.1%
 
4.27430.1%
 
6.71390.1%
 
4.88380.1%
 
3.66380.1%
 
1.83380.1%
 
9.76370.1%
 
7.93360.1%
 
Other values (62204)6826596.0%
 
ValueCountFrequency (%) 
024513.4%
 
0.0099999997761< 0.1%
 
0.012< 0.1%
 
0.052< 0.1%
 
0.050000000751< 0.1%
 
ValueCountFrequency (%) 
89691.773441< 0.1%
 
87179.611< 0.1%
 
80555.011< 0.1%
 
56873.961< 0.1%
 
54935.991< 0.1%
 

consumo_prom_no_dom
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct37440
Distinct (%)52.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.7601718
Minimum0
Maximum89691.77344
Zeros8109
Zeros (%)11.4%
Memory size555.5 KiB
2020-09-30T16:17:50.469268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.2754167
median19.28000034
Q354.186875
95-th percentile333.6616663
Maximum89691.77344
Range89691.77344
Interquartile range (IQR)47.9114583

Descriptive statistics

Standard deviation1095.817805
Coefficient of variation (CV)8.64481161
Kurtosis2364.161672
Mean126.7601718
Median Absolute Deviation (MAD)16.85000034
Skewness40.71654298
Sum9012901.734
Variance1200816.661
MonotocityNot monotonic
2020-09-30T16:17:50.631586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0810911.4%
 
1.223300.5%
 
1.832900.4%
 
3.052600.4%
 
4.272160.3%
 
7.932030.3%
 
3.662020.3%
 
4.882010.3%
 
6.11930.3%
 
6.711900.3%
 
Other values (37430)6090885.7%
 
ValueCountFrequency (%) 
0810911.4%
 
0.0099999997761< 0.1%
 
0.012< 0.1%
 
0.0121< 0.1%
 
0.014999999661< 0.1%
 
ValueCountFrequency (%) 
89691.773441< 0.1%
 
87179.611< 0.1%
 
80555.011< 0.1%
 
56873.961< 0.1%
 
54935.991< 0.1%
 

bimestre
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size69.5 KiB
2
23942 
3
23822 
1
23338 
ValueCountFrequency (%) 
22394233.7%
 
32382233.5%
 
12333832.8%
 
2020-09-30T16:17:50.793797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T16:17:50.887855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:50.992076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

consumo_total_no_dom
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct27336
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean436.0603092
Minimum0
Maximum119726.94
Zeros8109
Zeros (%)11.4%
Memory size555.5 KiB
2020-09-30T16:17:51.138693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.98
median54.055
Q3230.43
95-th percentile1695.6175
Maximum119726.94
Range119726.94
Interquartile range (IQR)219.45

Descriptive statistics

Standard deviation2126.152162
Coefficient of variation (CV)4.875821343
Kurtosis798.0749258
Mean436.0603092
Median Absolute Deviation (MAD)52.875
Skewness22.5073679
Sum31004760.1
Variance4520523.018
MonotocityNot monotonic
2020-09-30T16:17:51.331484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0810911.4%
 
1.224020.6%
 
1.833160.4%
 
3.053020.4%
 
7.932190.3%
 
1.182170.3%
 
4.882120.3%
 
4.272120.3%
 
3.662110.3%
 
6.11950.3%
 
Other values (27326)6070785.4%
 
ValueCountFrequency (%) 
0810911.4%
 
0.013< 0.1%
 
0.031< 0.1%
 
0.053< 0.1%
 
0.081< 0.1%
 
ValueCountFrequency (%) 
119726.941< 0.1%
 
117150.911< 0.1%
 
1010351< 0.1%
 
89691.81< 0.1%
 
88204.371< 0.1%
 

gid
Categorical

UNIQUE

Distinct71102
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
71102
 
1
23703
 
1
23697
 
1
23698
 
1
23699
 
1
Other values (71097)
71097 
ValueCountFrequency (%) 
711021< 0.1%
 
237031< 0.1%
 
236971< 0.1%
 
236981< 0.1%
 
236991< 0.1%
 
237001< 0.1%
 
237011< 0.1%
 
237021< 0.1%
 
237041< 0.1%
 
237121< 0.1%
 
Other values (71092)71092> 99.9%
 
2020-09-30T16:17:51.569418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique71102 ?
Unique (%)100.0%
2020-09-30T16:17:51.733511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length4.843801862
Min length1

indice_des
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size69.6 KiB
bajo
29248 
popular
16539 
alto
15516 
medio
9799 
ValueCountFrequency (%) 
bajo2924841.1%
 
popular1653923.3%
 
alto1551621.8%
 
medio979913.8%
 
2020-09-30T16:17:51.890862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T16:17:51.995422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:52.112403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length4
Mean length4.835644567
Min length4

latitud
Real number (ℝ≥0)

Distinct22930
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.39227276
Minimum19.13586653
Maximum19.57910261
Zeros0
Zeros (%)0.0%
Memory size555.5 KiB
2020-09-30T16:17:52.265282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum19.13586653
5-th percentile19.27217463
Q119.34407317
median19.39291026
Q319.44681849
95-th percentile19.49744601
Maximum19.57910261
Range0.4432360842
Interquartile range (IQR)0.1027453211

Descriptive statistics

Standard deviation0.07054946408
Coefficient of variation (CV)0.003638019377
Kurtosis-0.3299967947
Mean19.39227276
Median Absolute Deviation (MAD)0.05121505235
Skewness-0.2209675789
Sum1378829.378
Variance0.004977226881
MonotocityNot monotonic
2020-09-30T16:17:52.433220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
19.4954597821< 0.1%
 
19.5031486521< 0.1%
 
19.4488818321< 0.1%
 
19.5143312121< 0.1%
 
19.3009469921< 0.1%
 
19.5112685221< 0.1%
 
19.5108167821< 0.1%
 
19.4171689613< 0.1%
 
19.4966164613< 0.1%
 
19.5116013612< 0.1%
 
Other values (22920)7091799.7%
 
ValueCountFrequency (%) 
19.135866533< 0.1%
 
19.136289973< 0.1%
 
19.169514452< 0.1%
 
19.17289733< 0.1%
 
19.1739933< 0.1%
 
ValueCountFrequency (%) 
19.579102613< 0.1%
 
19.575032333< 0.1%
 
19.574567263< 0.1%
 
19.57185673< 0.1%
 
19.571418773< 0.1%
 

longitud
Real number (ℝ)

Distinct22930
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-99.13289588
Minimum-99.33770342
Maximum-98.95046917
Zeros0
Zeros (%)0.0%
Memory size555.5 KiB
2020-09-30T16:17:52.616585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-99.33770342
5-th percentile-99.2236241
Q1-99.17248433
median-99.13519579
Q3-99.09663337
95-th percentile-99.02915715
Maximum-98.95046917
Range0.3872342535
Interquartile range (IQR)0.07585096428

Descriptive statistics

Standard deviation0.05789023819
Coefficient of variation (CV)-0.0005839659749
Kurtosis0.03317853179
Mean-99.13289588
Median Absolute Deviation (MAD)0.0378663909
Skewness0.1247230301
Sum-7048547.163
Variance0.003351279677
MonotocityNot monotonic
2020-09-30T16:17:52.794001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-99.1375628621< 0.1%
 
-99.2042146521< 0.1%
 
-99.1582172821< 0.1%
 
-99.1436926121< 0.1%
 
-99.0890349321< 0.1%
 
-99.1858947221< 0.1%
 
-99.2075157421< 0.1%
 
-99.1707142613< 0.1%
 
-99.1930681313< 0.1%
 
-99.1412796112< 0.1%
 
Other values (22920)7091799.7%
 
ValueCountFrequency (%) 
-99.337703423< 0.1%
 
-99.327994133< 0.1%
 
-99.325920983< 0.1%
 
-99.325443263< 0.1%
 
-99.325025133< 0.1%
 
ValueCountFrequency (%) 
-98.950469173< 0.1%
 
-98.951286673< 0.1%
 
-98.953346343< 0.1%
 
-98.954080293< 0.1%
 
-98.957691983< 0.1%
 

Interactions

2020-09-30T16:17:33.624222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:33.729703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:33.848296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:33.953056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:34.064858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:34.176091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:34.282490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:34.390617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:34.503701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:34.619853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:34.738949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:34.954321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:35.091144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:35.210840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:35.340019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:35.494193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:35.615602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:35.736797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:35.868013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:35.992145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:36.127077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:36.232596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:36.354300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:36.467737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:36.579904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:36.691139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:36.797546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:36.902941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:37.015111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:37.120680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:37.239765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:37.356189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:37.481338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:37.595102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:37.714843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:37.835908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:37.949006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:38.062385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:38.182691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:38.298954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:38.426184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:38.539840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:38.781931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:38.901949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:39.021110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:39.140795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:39.254565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:39.370906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:39.492182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:39.608687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:39.736989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:39.843837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:39.960585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:40.065320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:40.176008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:40.291174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:40.403748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:40.513423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:40.637619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:40.743096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:40.864332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:40.970493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:41.088237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:41.194309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:41.308981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:41.421318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:41.526836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:41.633167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:41.745792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:41.853456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:41.972322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:42.086461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:42.212154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:42.329151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:42.450266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:42.570649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:42.685507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:42.800984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:42.922523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:43.037721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:43.165847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:43.418806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:43.543018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:43.648707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:43.761596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:43.875555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:43.981567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:44.088006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:44.201147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:44.308964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:44.429295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:44.552991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:44.691396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:44.816540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:44.947329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:45.078091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:45.202135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:45.327927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:45.459451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:45.584964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-09-30T16:17:52.967621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-30T16:17:53.196101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-30T16:17:53.404917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-30T16:17:53.608842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-09-30T16:17:53.790725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-09-30T16:17:45.876724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:46.231575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:46.504432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:17:46.644217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

consumo_total_mixtoanionomgeoconsumo_prom_domconsumo_total_domalcaldiacoloniaconsumo_prom_mixtoconsumo_totalconsumo_promconsumo_prom_no_dombimestreconsumo_total_no_domgidindice_deslatitudlongitud
0159.722019gustavo a. madero42.566364468.23gustavo a. madero7 de noviembre53.24000631.0042.0666673.05000033.0557250alto19.455260-99.112662
10.002019gustavo a. madero35.936667107.81gustavo a. madero7 de noviembre0.00000115.1328.7825007.32000037.3257253medio19.455260-99.112662
20.002019gustavo a. madero24.586000122.93gustavo a. madero7 de noviembre0.00000197.9632.99333375.030000375.0357255popular19.455720-99.113582
30.002019gustavo a. madero0.0000000.00gustavo a. maderonueva tenochtitlan0.00000253.5384.51000084.5100003253.5357267bajo19.459647-99.104469
456.722019azcapotzalco67.436250539.49azcapotzalcoprohogar56.72000839.3576.304545121.5700003243.1457330bajo19.474161-99.146750
5439.772019azcapotzalco35.675769927.57azcapotzalcotrabajadores del hierro54.971251399.6737.82891910.776667332.3357273bajo19.478613-99.150571
6991.802019azcapotzalco22.3818844633.05azcapotzalcobarrio coltongo123.975007693.6433.305801129.29937532068.7957275bajo19.480211-99.152316
70.002019azcapotzalco0.0000000.00azcapotzalcobarrio coltongo0.00000305.00152.500000152.5000003305.0057276popular19.479096-99.148920
8184.862019azcapotzalco33.6611761716.72azcapotzalcotrabajadores del hierro46.215001903.6633.9939292.08000032.0857277bajo19.478585-99.148847
910.982019azcapotzalco51.912500207.65azcapotzalcotrabajadores del hierro10.98000237.5429.6925006.303333318.9157281bajo19.477273-99.147921

Last rows

consumo_total_mixtoanionomgeoconsumo_prom_domconsumo_total_domalcaldiacoloniaconsumo_prom_mixtoconsumo_totalconsumo_promconsumo_prom_no_dombimestreconsumo_total_no_domgidindice_deslatitudlongitud
71092NaN2019cuauhtemoc18.328530623.17cuauhtemocdoctoresNaN1148.6722.97340032.8437511525.50219medio19.424418-99.144312
71093226.042019cuauhtemoc13.793529234.49cuauhtemoccentro113.019997718.3119.95305615.1635301257.78221medio19.431082-99.143493
7109453.742019cuauhtemoc24.4660172886.99cuauhtemocguerrero26.8700013052.0421.3429374.8395651111.31231bajo19.449404-99.138990
71095749.402019cuauhtemoc21.0162377818.03cuauhtemocguerrero107.0571438749.2722.15002511.3637501181.82230bajo19.449682-99.140397
7109668.682019cuauhtemoc22.5849106301.20cuauhtemocguerrero68.6800006690.7823.39430153.4833351320.90234bajo19.449003-99.143680
71097359.882019cuauhtemoc282.1524881128.61cuauhtemocguerrero179.9400021509.15167.6833286.886667120.66236bajo19.450079-99.144435
71098401.322019cuauhtemoc25.0214422777.38cuauhtemocguerrero100.3300003318.6327.42669423.3216671139.93240bajo19.448210-99.144851
71099142.252019cuauhtemoc27.0436541406.27cuauhtemocguerrero28.4500011586.6125.5904847.618000138.09241popular19.447826-99.143819
7110031.422019cuauhtemoc18.6015293162.26cuauhtemocguerrero15.7100013250.3918.2606189.451667156.71243bajo19.448187-99.142392
71101976.122019cuauhtemoc23.9496998765.60cuauhtemocguerrero162.6866699858.4626.01174116.6771431116.74246bajo19.447683-99.141193